Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)

• Main Authors: Miguel de Domingo, Nuria Ortigosa, Javier Sevilla, Sandra Roger
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Sensors
• Subjects: fire prevention; artificial intelligence; k-means; DBSCAN; Floyd–Warshall
• Online Access: https://www.mdpi.com/1424-8220/21/3/797

Forest fires are undesirable situations with tremendous impacts on wildlife and people’s lives. Reaching them quickly is essential to slowing down their expansion and putting them out in an effective manner. This work proposes an optimized distribution of fire stations in the province of Valencia (Spain) to minimize the impacts of forest fires. Using historical data about fires in the Valencia province, together with the location information about existing fire stations and municipalities, two different clustering techniques have been applied. Floyd–Warshall dynamic programming algorithm has been used to estimate the average times to reach fires among municipalities and fire stations in order to quantify the impacts of station relocation. The minimization was done approximately through <i>k</i>-means clustering. The outcomes with different numbers of clusters determined a predicted tradeoff between reducing the time and the cost of more stations. The results show that the proposed relocation of fire stations generally ensures faster arrival to the municipalities compared to the current disposition of fire stations. In addition, deployment costs associated with station relocation are also of paramount importance, so this factor was also taken into account in the proposed approach.